The landscape of conventional and artificial intelligence-based clinical prediction models in non-small-cell lung cancer: from development to real-world validation.

Globally, lung cancer remains the most common cause of cancer mortality, with non-small-cell lung cancer (NSCLC) being the most common subtype of lung cancer diagnosed. This review paper provides a comprehensive landscape of clinical prediction models (CPMs) in NSCLC, including in early-stage and metastatic disease, and the recent acceleration of artificial intelligence integration. Prediction models are developed using multimodal patient data to allow oncologists to make evidence-based decisions regarding patient treatment options.

Combined Flt3L and CD40 agonism restores dendritic cell-driven T cell immunity in pancreatic cancer.

T cell-directed immunotherapies have largely failed to slow progression of pancreatic ductal adenocarcinoma (PDAC) because of poor tumor antigenicity and an immunosuppressive tumor microenvironment. We hypothesized that conventional dendritic cell (cDC) suppression in PDAC may further impair tumor immunity. We found that low tissue expression of Fms-like tyrosine kinase 3 ligand (Flt3L) partially underlies cDC deficits.

Democratizing cardiac imaging with an automated magnetic resonance exam.

Advanced imaging of the heart, including cardiovascular magnetic resonance imaging (CMR), has revolutionized the diagnosis and prognosis for cardiovascular disease. For the past 40 years, CMR has primarily relied on the acquisition of numerous breath-held 2D images resulting in complex scanner operation, patient discomfort, long scan durations, and cumbersome image interpretation. These limitations constrain CMR use to major academic hospital systems and severely limit patient access to CMR, which makes up < 1% of total cardiovascular imaging despite being represented in two thirds of all AHA/ACC guidelines.

Atom Identification in Bilayer Moiré Materials with Gomb-Net.

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern.

Early use of the novel antifungal rezafungin: a case series and literature review.

Objectives: Rezafungin is a novel echinocandin with a unique structural configuration enabling weekly IV dosing. We report on early use of rezafungin in our outpatient parenteral antibiotic therapy (OPAT) service, reviewing indications, treatment regimens, outcomes and adverse events in adult patients receiving rezafungin at a tertiary infectious disease centre. We also review published cases of rezafungin use, licensing trials, spectrum and pharmacokinetics/pharmacodynamics and how that might relate to its propensity to generate resistance (in comparison with daily echinocandins).